This paper presents BitLight, a novel paradigm that uses the rapid flashing of a digital light processing (DLP) projector to encode an imperceptible mask temporally that, when sensed by a photodiode, uniquely specifies where the photodiode is located on the projected image. BitLight is inspired by the psychophysical phenomenon that the human visual system (HVS) cannot resolve rapid temporal changes in optical signals, so redundant optical signals could be inserted for tracking with some or little compromise on the original human perceived visual content. BitLight is the first to devise a bit-level temporal encoding to display RGB colors while also embedding tracking signals in a digital fashion. Compared to traditional visible-light-communication (VLC) systems that use frame-level encoding techniques such as luminance changes [10, 25, 26, 32] and alpha channel [16], the bit-level encoding of BitLight makes better use of the ultra-fine temporal division capabilities of DLP projectors to embed a much higher tracking data throughput and thus achieve faster localization speed. With our current prototype hardwares of a low-end microcontroller, cheap photodiodes, and a commercial off-the-shelfDLP projector, evaluation results have demonstrated an average of only 9.5ms to localize the sensor, versus 200ms by a comparison testbed that uses simple frame-level encodings.

Many existing indoor localization systems have achieved applaudable performance through comprehensive modeling of its envisioned working scenario. However their real life deployment are often prohibited by high deployment overhead and performance degradation in dynamic environments. This paper presents SmartLight, a 3D digital indoor localization system based on LED lighting infrastructures. It adopts a novel design philosophy of shifting all the complexity into modifying a single LED lamp and maintaining minimum complexity on the receiver to reduce the hassle on system deployment/calibration. With a single modified LED lamp, the system is capable of localizing a large number of light sensors in a room. The underlying technique is to exploit the light splitting properties of convex lens to create an one-to-one mapping between a location and the set of orthogonal digital light signals receivable at that location. Advanced designs are also introduced to further improve the system accuracy and scalability beyond the hardware capability. In evaluating the design, we build an experimental prototype with a 60hz projector, achieving average localization around 10cm and 90 percentile error of 50cm.

Dynamic spectrum access has been proposed as a means to share scarce radio resources, and requires devices to follow protocols that access spectrum resources in a proper, disciplined manner. For a cognitive radio network to achieve this goal, spectrum policies and the ability to enforce them are necessary. Detection of an unauthorized (anomalous) usage is one of the critical issues in spectrum etiquette enforcement. In this paper, we present a network structure for dynamic spectrum access and formulate the anomalous usage detection problem using statistical significance testing. The detection problem investigated considers two cases, namely, the authorized (primary) transmitter is (i) mobile and (ii) fixed. We propose a detection scheme for each case by exploiting the spatial pattern of received signal energy across a network of sensors. Analytical models are formulated when the distribution of the energy measurements is given and, due to the intractability of the general problem, we present an algorithm using machine learning techniques to solve the general case when the statistics of the energy measurements are unknown. Our simulation results show that our approaches can effectively detect unauthorized spectrum usage with a detection probability above 0.9 while keeping the false alarm rate less than 0.1 when only one unauthorized radio is present, and the detection probability is even higher for more unauthorized radios.

We analyze the radio interference from a Broadband Power Line (BPL) system operating between 2 MHz and several tens of MHz. The overhead medium-voltage (MV) power line is modeled as a 3-phase set of parallel wires above a lossy earth. A near-exact solution, based on previous approaches for infinitely long lines, is presented for the fields from arbitrarily long lines. Emissions are computed by considering a specified BPL model of semi-infinite length, and the maximum allowable excitation voltage vs. frequency is computed by assuming compliance with FCC field strength limits. These calibration results are used to study the interference to local terrestrial services and to quantify both the theoretical capacity and practical throughputs for BPL systems operation over [2, 30] MHz and [32, 60] MHz. We conclude that practical BPL throughputs are attainable while meeting current FCC requirements, but that these limits may not be stringent enough to avoid serious interference to neighboring radio services.

The openness of the lower-layer protocol stacks in cognitive radios increases the flexibility of dynamic spectrum access and promotes spectrally-efficient communications. To ensure the effectiveness of spectrum sharing, it is desirable to locate primary users, secondary users, and unauthorized users in a non-interactive fashion based on limited measurement data at receivers. In this work, we present two range-free localization algorithms based on dynamic mapping of received signal strength (RSS) to perform non-interactive localization that does not require the cooperation from the cognitive device to be located. A fine-grained signal strength map across the surveillance area is constructed dynamically through interpolation. By making use of this signal map, the proposed schemes can achieve higher accuracy of location estimation than existing noninteractive and RSS based methods in most channel variation conditions. Both our simulation results as well as testbed evaluations have demonstrated the feasibility of the proposed algorithms.

This work investigates the lower bounds of wireless localization accuracy using signal strength on commodity hardware. Our work relies on trace-driven analysis using an extensive indoor experimental infrastructure. First, we report the best experimental accuracy, twice the best prior reported accuracy for any localization system. We experimentally show that adding more and more resources (e.g., training points or landmarks) beyond a certain limit, can degrade the localization performance for lateration-based algorithms, and that it could only be improved further by "cleaning" the data. However, matching algorithms are more robust to poor quality RSS measurements. We next compare with a theoretical lower bound using standard Cramer Rao Bound (CRB) analysis for unbiased estimators, which is frequently used to provide bounds on localization precision. Because many localization algorithms are based on different mathematical foundations, we apply a diverse set of existing algorithms to our packet traces and found that the variance of the localization errors from these algorithms are smaller than the variance bound established by the CRB. Finally, we found that there exists a wide discrepancy from what free- space models predict in the signal to distance function even in an environment with limited shadowing and multipath, thereby imposing a fundamental limit on the achievable localization accuracy indoors.

Dynamic spectrum access has been proposed as a means to share scarce radio resources, and requires devices to follow protocols that use resources in a proper, disciplined manner. For a cognitive radio network to achieve this goal, spectrum policies and the ability to enforce them are necessary. Detection of an unauthorized (anomalous) usage is one of the critical issues in spectrum etiquette enforcement. In this paper, we present a network structure for dynamic spectrum access and formulate the anomalous usage detection problem using statistical significance testing. The detection problem is classified into two subproblems. For the case where no authorized signal is present, we describe the existing cooperative sensing schemes and investigate the impact of signal path loss on their performance. For the case where an authorized signal is present, we propose three methods that detect anomalous transmissions by making use of the characteristics of radio propagation. Analytical models are formulated for two special cases and, due to the intractability of the general problem, we present an algorithm using machine learning techniques to solve the general case. Our simulation results show that our approaches can effectively detect unauthorized spectrum usage with high detection rate and low false positive rate.

We investigate the measurement, interference and capacity issues of the overhead medium-voltage (MV) broadband over power line (BPL) systems operating between 1.7 and 80 MHz. The MV power lines are modeled as a 3-phase set of parallel wires with infinite length and above a lossy earth. Electric field strength is calculated. Two important parameter settings are addressed for the measurement of BPL emission field, namely, the measurement height and the extrapolation factor. The latter is defined as the falloff of field strength, in dB/decade, with lateral distance from the power line. BPL interference is evaluated in terms of the 3-dB critical distance, i.e., the lateral distance at which the total noise into a radio receiver is increased by 3 dB. Using this metric, we explore the relationship between capacity and BPL interference potential.

We analyze and quantify the radio interference to aeronautical receivers from a massively deployed broadband over power line (BPL) system. Based on our previous semi-finite multi-line model, a closed-form solution is derived for the far field using the saddle-point method. We investigate the aggregate radiation power from a circular area in which randomly oriented BPL spans are uniformly placed. A scaling rule for the radiation power is discovered, with respect to both the number of BPL spans and the altitude of the receivers. The level of interference is numerically evaluated for a baseline case. The result can be generalized using the scaling rule derived here.